{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving Plots to a File\n",
    "Saving plots in ggplot is easy. Just use the `save` method of your ggplot object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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picnJyahUKvU+dVq9vb3iFEhpta9NpVIp+vv7Y3p62ut3i7PW7aFUKjV7hLpySQAAAKkJ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\nQGqCFQCA1AQrAACp/T+0piSX12sDrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f409f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (284428741)>\n"
     ]
    },
    {
     "data": {
      "image/png": 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picnJyahUKvU+dVq9vb3iFEhpta9NpVIp+vv7Y3p62ut3i7PW7aFUKjV7hLpySQAAAKkJ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\nQGqCFQCA1AQrAACp/T+0piSX12sDrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11011a690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = ggplot(aes(x='mpg'), data=mtcars) + geom_histogram()\n",
    "print p\n",
    "p.save(\"myplot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And voilà. If you take a look at `myplot.png` you can see that we've saved our plot to a file\n",
    "```\n",
    "# markdown syntax for opening an image\n",
    "![](./myplot.png)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./myplot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
